Biomarkers for cardiovascular side-effects induced by cox-2 inhibitory compounds

ABSTRACT

The present invention is directed to methods for detecting the presence of minimal or early vasculitis or other vasculopathies induced by a cox-2 inhibitor in a subject to whom a cox-2 inhibitor has been administered, selection of cox-2 inhibitory compounds, use of a cox-2 inhibitory compounds in the manufacture of anti-inflammatory medicaments, and vaccination strategies.

FIELD OF THE INVENTION

The invention relates generally to the in vivo testing of the efficacy of a compound or composition, and particularly to the testing and biologically functionalizing of cox-2 inhibitory compounds (coxibs) by activity in vivo.

BACKGROUND OF THE INVENTION

Use of cox-2 specific inhibitory compounds (coxibs) and some NSAIDs has been associated with an increased risk of cardiovascular events in human including deep venous thrombosis, myocardial infarction, stroke, and sudden death. The current hypothesis is that some of anti-inflammatory compounds inhibit PGI2 synthesis but not TxA synthesis, altering the homeostatic balance towards the pro-coagulative/pro-trombotic pathways. Fitzgerald G A. N Engl J Med. 351(17):1709-11 (Oct. 21, 2004). It has been reported that some of anti-inflammatory compounds, mainly cox-2 inhibitors, inhibit PGI2 synthesis only, resulting in altered homeostatic balance towards the pro-coagulative pathways which in rare cases might lead to the serious cardiovascular side effects in human. Furberg C D, Psaty B M, FitzGerald G A. Circulation. 111(3):249 (Jan. 25, 2005).

There continues to be a need in the art for additional information about the cardiovascular side effects of the use of cox-2 specific inhibitory compounds.

SUMMARY OF THE INVENTION

A 2-week analysis in cynomolgus monkeys (Macaca fascicularis) treated with the coxibs COX189 (Lumiracoxib®, Novartis), refocoxib (Vioxx®, Merck), and celecoxib (Celebrex®, Pharmacia/Pfizer), and with the nonselective NSAID, diclofenac (Voltaren®, Novartis) showed that the Vioxx®-treated animals exhibit a specific mRNA expression pattern which shows the presence of an intravascular procoagulative/prothrombotic state particularly in venous vessels of a Vioxx®-treated monkey. The specific genomic pattern includes gene expression changes involved in blood and endothelial cell (EC) activation, interaction of blood cells with EC, activation of INFγ pathway, and release of pro-inflammatory cytokines and chemo-attractants. These data together with biochemical and histopathological findings indicate that Vioxx® induces or worsens the pro-coagulative/pro-thombotic changes, along with the activation of INFγ pathways triggered most probably by a endothelium tropic viral infection (e.g., cytomegalovirus (CMV)) and/or other vascular INFγ/TNF inducing situations (e.g., autoimmune vascular disorders).

The overall genomic findings show that Cox-2/PGE2 inhibition results in strong and uncontrolled induction of INFγ regulated chemo-attractants, adhesion molecules, and proinflammatory/pro-coagulative molecules which might lead to or increase the risk of cardiovascular adverse events. Histopathological results confirmed the genomic findings showing that the specific genomic pattern is an early signature of vasculitis and is observed only in the animal treated with Vioxx®.

Accordingly, the invention provides biomarkers (in the form of genomic information and serum or plasma proteins) for minimal and early vasculitis or other vasculopathies. In addition, the invention provides biomarkers for predicting potential Vioxx®-induced cardiovascular adverse effects.

Identification of biomarkers advantageously allows safe use of cox-2 inhibitory compounds in clinics and selection of cox-2 inhibitory follow-up compounds without cardiovascular toxicity. Indeed, the expression of several genes increased in the vessels of the Vioxx®-treated animal encode for secreted proteins, e.g., chemokine (CXC motif) ligand 10 (CXCL10) and other cytokines, which can be measured in peripheral samples such as blood or urine. Clinical screening of patients prior to, or during administration of Cox-2 inhibitory therapies should increase their safety profile.

Monitoring of early changes is predictive of cardiovascular adverse effects in patients treated with compounds exhibiting cox-2 inhibition or increasing the production of molecules induced by interferons, by virus infections, or autoimmune disorders resulting in pro-coagulative/prothrombotic/endothelium changes. These compounds include mainly cox-2 inhibitors, classical NSAIDs, other anti-inflammatory compounds and direct PGE2, cAMP and PKA inhibitors.

In one aspect of the invention, the data of the present invention identifies another pathway than the PGI2 synthesis pathway that may be one of the main triggering factors leading to the observed adverse cardiovascular events in human. Alteration in this pathway can be easily monitored in preclinical and clinical studies to avoid such cardiovascular side effects upon cox-2 and/or NSAIDs treatments. Biomarkers or the gene signature identified in this invention can also be used to monitor viral infection/INFγ pathway activation and some vasculopathies in diverse human diseases including several autoimmune and neurodegenerative disorders with or without anti-inflammatory and immunosuppressive treatments. Some of the biomarkers can be used for selection of compounds without potential cardiovascular side-effects.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1. Principal Component Analysis (PCA) of genomic data from six cardiovascular tissues: iliac vein, pulmonary vein, aorta, carotid artery, heart ventricle, and heart atrium. Only genes encoding for MHC molecules and their receptors were included for PCA analysis. The Vioxx®-treated monkey #A60055 (circled) exhibited distinct expression pattern.

FIG. 2. Specific genomics expression pattern in Vioxx®-treated monkey #A60055. The pattern consisted of transcripts for MHC class I, II & class I, non classical molecules, their receptors (TcRs and NK receptors), chemokines (CXCL9, -10, -11, MCP-1). Overall signature indicating strong INF pathway activation together with IL1/TNF and coagulation and complement pathways alteration.

FIG. 3. Histopathological evaluation of samples from different tissues confirms the genomic data showing focal vascular necrosis in the veins of Vioxx®-treated animal #A60055 only. The main findings consisted of EC necrosis, leucocytes/fibrin adhesion to EC surface, fibrinoid degeneration of the media and medial leukocyte infiltration. (A) Iliac vein from vehicle treated animal. (B) Histopathology findings of endothelial cell (EC) necrosis, fibrin leukocyte adhesion to EC surface, fibrinoid degeneration of the media, medial leukocytes infiltration in iliac vein of the monkey #A60055.

FIG. 4. Strong increase of CXCL10 in veins followed by arteries and heart samples from the Vioxx®-treated monkey #A60055 (indicated by an arrow) only.

FIG. 5. Protein profiling in serum and plasma from the monkeys. The monkey #A60055 exhibit a specific protein expression profile: Soluble MHC molecules b2-m, other chemokines, cytokines (INFγ, CXCL10, MCP-1, IL18, TNF RII, IL1b), and soluble VCAM-1. Human MAP is used to assess monkey proteins in a Rules-Based Medicine (RBM®) multiplex assay.

FIG. 6. ELISA confirmation of CXCL10 (IP10) protein level in monkey serum samples. The Vioxx®-treated monkey #A60055 exhibits the highest level of CXCL10 protein expression.

FIG. 7. ELISA confirmation of INFγ protein level in monkey serum and plasma samples. The Vioxx®-treated monkey #A60055 exhibits the highest level of INFγ protein expression.

FIG. 8. Localisation of PD-ECGF1 protein at the site of vascular lesion.

DETAILED DESCRIPTION OF THE INVENTION Introduction and Overview

The classical discovery process in the pharmaceutical industry is based on targets (enzymes, receptors, cellular assays, animal and disease models, etc.). Chemicals or biological products are tested, in a high-throughput mode, on a battery of pre-selected different targets. The weakness of the classical approach are the “artificially disconnected” in vitro target models compared to the tightly interconnected and interdependent relationship of the different targets in a whole organism and the fact that biological activity on all non selected targets is missed.

By contrast, the invention is a “non pre-conceived hypothesis” discovery process to rapidly identify and analyze the biological activity of new products in the whole organism, multi-organs and whole transcriptome. All physiological interactions between the different organs or tissues are present and any cellular pathway or any potential targets could potentially be analyzed in a non artificial system.

The data of the invention derived from this comparative multi-organ genomics analysis, coupled with extensive clinical, biochemical and histopathological data, identified a new pathway which may play the major role in the cardiovascular events observed in human treated with cox-2 inhibitors. The mRNA expression changes have been analyzed in several tissue samples from Macaca fascicularis following treatment with the Cox-2 specific inhibitors COX189 (Lumiracoxib®, Novartis), Refocoxib (Vioxx®, Merck), and Celecoxib (Celebrex®, Pharmacia/Pfizer), and with the nonselective NSAID, Diclofenac (Voltaren®, Novartis).

Administration of Compounds.

A two-week oral-gavage treatment with the Cox-2 specific inhibitor COX189 (Lumiracoxib®, Novartis) in comparison with refocoxib (Vioxx®, Merck), and celecoxib (Celebrex®, Pharmacia/Pfizer), and with the nonselective NSAID, diclofenac (Voltaren®, Novartis) was performed. All test items were administered to monkeys at doses higher than those used in patients to analyse mRNA expression changes in terms of mechanisms of drug actions and also potential cardiovascular toxic effects. The test items were administered daily at doses of 100 mg/kg/day, except Vioxx® which was administered at 50 mg/kg/day.

In one embodiment of the invention, the test animal is a vertebrate. In a particular embodiment, the vertebrate is a mammal. In a more particular embodiment, the mammal is a primate, such as a cynomolgus monkey (Macaca fascicularis). As used herein, the administration of an agent or drug to a subject includes self-administration and the administration by another.

In more particular embodiments, the “treatment group” of animals received a substance (test item, compound, drug) in a vehicle compound suitable for administration of the substance or the combination of substances, while the “control” (or “baseline”) group should receive the vehicle compound only. During the treatment period biological specimen such as tissue pieces (e.g. obtained by biopsy), or body fluids, such as blood, plasma, serum, urine, or saliva, can be sampled. At the end of the treatment time all animals of all groups can be sacrificed and biological specimen such as whole organs or pieces thereof can be sampled. All sampled specimen can be stored as known in the art for further analysis that include, but are not limited to, RT-PCR, Northern blotting, in-situ hybridization, gene expression profiling with microarrays.

In one embodiment, the invention begins with differentially expressed transcripts in different cardiovascular tissues and proteins in plasma between normal monkeys and cox-2 inhibitory compounds/drugs-treated monkeys with regard to the identification and validation of potential targets and the identification of biomarkers for cardiovascular side effects.

Gene Expression Profiles.

After a period of time (e.g., four weeks) of compound/drug administration, the treated animals are necropsied. 120 tissues are dissected and rapidly snap-frozen for genomics analysis. Organ samples are isolated for histopathological examinations and for gene expression localizations, such as by in situ hybridization.

In more particular embodiments, the methods of detecting the level of expression of mRNA are well-known in the art and include, but are not limited to, reverse transcription PCR, real time quantitative PCR, Northern blotting and other hybridization methods. A particularly useful method for detecting the level of mRNA transcripts obtained from a plurality of genes involves hybridization of labelled mRNA to an ordered array of oligonucleotides. Such a method allows the level of transcription of a plurality of these genes to be determined simultaneously to generate gene expression profiles or patterns.

As used herein, a gene expression profile is diagnostic when the increased or decreased gene expression is an increase or decrease over the baseline gene expression following administration of a compound.

In one embodiment, the technique for detecting gene expression includes the use of a gene chip. The construction and use of gene chips are well known in the art. See, U.S. Pat. Nos. 5,202,231; 5,445,934; 5,525,464; 5,695,940; 5,744,305; 5,795,716 and 5,800,992. See also, Johnston, M. Curr Biol 8:R171-174 (1998); Iyer V R et al., Science 283:83-87 (1999) and Elias P, “New human genome ‘chip’ is a revolution in the offing” Los Angeles Daily News (Oct. 3, 2003).

Additional procedures that can be used in the methods of the invention are described in PCT/EP2004/012572, “USE OF ORGANIC COMPOUND”, filed Nov. 11, 2004, incorporated herein by reference).

Gene expression profiles have been generated using the Affymetrix microarray technology. (i) RNA extraction and purification: Briefly, total RNA was obtained by acid guanidinium thiocyanate-phenol-chloroform extraction (Trizol®, Invitrogen Life Technologies, San Diego, Calif.) from each frozen tissue section and the total RNA was then purified on an affinity resin (Rneasy®, Qiagen) according to the manufacturer's instructions. Total RNA was quantified by the absorbance at λ=260 nm (A_(260 nm)) and the purity was estimated by the ratio A_(260 nm)/A_(280 nm). Integrity of the RNA molecules was confirmed by non-denaturing agarose gel electrophoresis. RNA was stored at −80° C. until analysis. One part of each individual RNA sample was kept for the analysis of critical genes by means of Real-time PCR. (ii) GeneChip® experiment: All GeneChip® experiments were conducted in the Genomics Factory EU following recommendations by the manufacturer of the GeneChip® system (Affymetrix, Expression Analysis Technical Manual (Affymetrix, Santa Clara, Calif., 2005). Human U133A genome arrays were used for transcript expression analysis. Double stranded cDNA was synthesized with a starting amount of approximately 5 μg full-length total RNA using the Superscript Choice System (Invitrogen Life Technologies) in the presence of a T7-(dT) 24 DNA oligonucleotide primer. Following synthesis, the cDNA was purified by phenol/chloroform/isoamyl alcohol extraction and ethanol precipitation. The purified cDNA was then transcribed in vitro using the BioArray® High Yield RNA Transcript Labeling Kit (ENZO) in the presence of biotinylated ribonucleotides form biotin labelled cRNA. The labelled cRNA was then purified on an affinity resin (Rneasy, Qiagen), quantified and fragmented. An amount of approximately 10 μg labelled cRNA was hybridized for approximately 16 hours at 45° C. to an expression probe array. The array was then washed and stained twice with streptavidin-phycoerythrin (Molecular Probes) using the GeneChip Fluidics Workstation 400 (Affymetrix). The array was then scanned twice using a confocal laser scanner (GeneArray Scanner®, Agilent) resulting in one scanned image. This resulting “.dat-file” was processed using the MASS program (Affymetrix) into a “.cel-file”. The “.cel file” was then transferred to tan Affymetrix GeneChip Laboratory Information Management System (LIMS) database, which is connected to a UNIX Sun Solaris server through a network filing system that allows for the average intensities for all probes cells (CEL file) to be downloaded into an Oracle database (NPGN). Raw data was converted to expression levels using a “target intensity” of 100. The numerical values displayed are weighted averages of the signal intensities of the probe-pairs comprised in a probe-set for a given transcript sequence (AvgDiff value). The data were checked for quality and loaded in the GeneSpring® software versions 5.0 (Silicon Genetics, Calif., US) for statistical analysis.

Quality Control Analysis of Transcriptome Data:

The following quality measures were analysed for each sample: Scaling factor, background, percent present calls, AFFX-GAPDH 3′:AFFX-GAPDH 5′-ratio, AFFX-GAPDH 3′ variance, AFFX-Beta-actin 3′:AFFX-Beta-actin 5′-ratio. Biological outliers and tissue contamination were identified using NPGN-database Gene Expression Tools by comparing the average signal intensity per probe set per treatment group to the signal intensity in each sample. Attention was paid to the homogeneity of the data. Average and standard deviation of the background noise level determined the raw data restriction value used in the consequent analysis.

Principal Component Analysis of Transcriptome Data:

Using SIMCA 10.5 software (Umetrics Inc, Kinnelon N.J., USA), Principal Component Analysis (PCA) was performed on all data generated by the microarrays or on, genes present at least in 2 out of 4 samples in at least 1 group to determine general expression differences/similarities among the samples and identify potential biological or technical outliers. A projection was made on the first two or three principal components for each tissue. Here, the differences between samples represent differences in the level of expression or in the correlation structure of the genes used for the PCA model.

The information was further refined by the use of complementary techniques. In situ hybridization, for example, can indicate precisely which cell type inside an organ is specifically expressing a given gene. This technique based on the detection of RNA is independent of the availability of an antibody. Quantitative PCR has also been used to confirm expression levels of particular genes of interest.

To obtain biomarkers predicting cardiovascular adverse effect of tested compounds/drugs, expression levels of proteins have been analysed in cynomolgus monkey serum and plasma from the present analysis using human Multi-Analyte Profile (MAP) Technology. Human MAP could be used to measure protein levels of more than 80 antigens in monkey serum and plasma (Rules-Based Medicine Inc (RBM®), Austin, Tex. USA).

The following EXAMPLE is presented in order to more fully illustrate the preferred embodiments of the invention. This EXAMPLE should in no way be construed as limiting the scope of the invention, as defined by the appended claims.

Example Identification of Specific Genomics Signature in Vioxx®-Treated Monkey(s)

Overall genomics data obtained for 16 tissues from all monkey groups showed that the Vioxx®-treated animals exhibit a specific pattern of gene expression. This pattern includes significant increases (ANOVA, p<0.05) in the expression of MHC class I classical and non-classical molecules, MHC class II molecules and their respective receptors such as TcRs and Immunoglobulin-like molecules.

Analysis of genomic data from several cardiovascular tissues by Principle Component Analysis (PCA) on the selected genes composed of MHC molecules identified a biological outlier (Animal no: A60055, circled in the FIG. 1) within the Vioxx®-treated group.

Further analysis of all genomic data by PLS-DA provided a list of the most discriminate genes between the animal A60055 and the rest of the animals from Vioxx®, Celebrex®, Cox189 (Novartis), diclofenac and vehicle treated groups (TABLE 1, FIG. 2). The specific gene pattern included mainly interferon inducible genes encoding for Toll like receptors (TLRs), classical and non-classical MHC class I, MHC class II, their respective receptors/ligands such as TcRs and NK receptors, several chemokines such as CXCL10, CCL2, an extensive list of INFγ pathways signalling genes such as Jak1, Stat1, and some IL1/TNF pathway related molecules. In addition, there was strong and significant increases in the expression of coagulation pathways related molecules such as PD-ECGF, coagulation factor II (thrombin) receptor-like 1, Factor 13 A1, several adhesion molecules such as VCAM and ICAM, and a number of genes belonging to the complement activation and other pathways innate immunity pathways. This genomic expression pattern predominant in the vessels of the Vioxx®-treated monkey (#A60055) indicated development of a potential vasculopathy/vasculitis with strong activation of INFγ pathway suggestively induced by an endothelium tropic infection or reactivation of a vascular autoimmune disorder.

Interestingly, histopathological evaluation of all tissues showed clear sign of vasculitis in veins only of the Vioxx®-treated animal A60055 (FIG. 3). Thus the specific expression pattern should be a specific genomics signature of minimal vasculitis (see below).

The role of Vioxx®-induced cox-2 inhibition in the observed genomic and histopathological findings provide a potential link to the increased risks of cardiovascular side effects occurring in patients treated with Vioxx®: The majority of the observed gene expression changes have been known to be directly involved in the pathogenesis of diverse cardiovascular diseases including atherosclerosis, CAD, thrombosis, autoimmune and neurodegenerative diseases. Among the INFγ inducible gene expression changes, the most striking increase was observed for CXCL10 and other chemokines, e.g., CXCL-9, -11 and MCP-1 (CCL-2) (FIG. 4 and TABLE 1).

TABLE 1 The most discriminant genes for Vioxx animal #A60055 and corresponding genomics expression data from iliac vein samples of monkeys treated with vehicle, Vioxx ®, Celebrex ®, Cox189 (Novartis), and diclofenac. These results indicated potential vasculopathies in the animal A60055, probably induced by an unknown virus infection together with an exaggerated host immune response against vascular endothelium. Vioxx without Systematic Control Vioxx A60055 Celebrex Cox189 Voltaren Name SYMBOL GENENAME Avg SD A60055 Avg fold changes vs control 216598_s_at CCL2 chemokine (C-C motif) ligand 2 6 1 150.9 1.0 2.3 1.6 9.0 202411_at IFI27 interferon, alpha-inducible protein 27 20 12 20.8 3.8 3.3 4.2 5.6 204533_at CXCL10 chemokine (C—X—C motif) ligand 10 74 20 19.8 1.1 1.1 1.4 1.3 209969_s_at STAT1 signal transducer and activator of 50 16 13.5 1.7 2.7 1.4 2.3 transcription 1, 91 kDa 212998_x_at HLA-DQB2 major histocompatibility complex, 252 86 10.8 1.2 1.9 1.4 2.2 class II, DQ beta 2 210163_at CXCL11 chemokine (C—X—C motif) ligand 11 4 5 9.7 1.5 3.7 2.6 2.1 203915_at CXCL9 chemokine (C—X—C motif) ligand 9 64 21 9.3 1.6 1.4 1.7 1.3 214038_at CCL8 chemokine (C-C motif) ligand 8 19 13 8.8 1.8 1.3 1.7 1.8 214453_s_at IFI44 interferon-induced protein 44 141 24 8.2 2.5 1.3 2.0 1.7 212671_s_at HLA-DQA1 major histocompatibility complex, 445 167 8.2 1.0 2.1 1.6 1.2 class II, DQ alpha 1 211654_x_at HLA-DQB1 major histocompatibility complex, 544 143 7.7 1.0 2.0 1.8 1.6 class II, DQ beta 1 AFFX- STAT1 signal transducer and activator of 109 25 7.1 1.5 1.2 1.9 1.1 HU- transcription 1, 91 kDa MISGF3A/ M97935_MB_at 213797_at cig5 viperin 25 17 7.1 1.4 1.3 1.8 2.1 211122_s_at CXCL11 chemokine (C—X—C motif) ligand 11 10 9 6.6 1.0 1.6 2.3 2.0 210029_at INDO indoleamine-pyrrole 2,3 dioxygenase 53 16 6.5 1.1 1.6 1.2 1.4 214567_s_at XCL1 chemokine (C motif) ligand 1 13 12 5.4 1.1 3.5 1.3 0.5 AFFX- STAT1 signal transducer and activator of 99 7 5.3 1.3 1.3 1.2 1.0 HUMISGF3A/ transcription 1, 91 kDa M97935_MA_at 203153_at IFIT1 interferon-induced protein with 100 35 5.0 1.5 0.8 1.1 1.0 tetratricopeptide repeats 1 217502_at IFIT2 interferon-induced protein with 168 62 4.7 1.3 1.1 1.4 0.8 tetratricopeptide repeats 2 205483_s_at G1P2 interferon, alpha-inducible protein 27 13 4.5 3.3 1.4 1.5 2.1 (clone IFI-15K) 206366_x_at XCL1 chemokine (C motif) ligand 1 33 15 4.3 1.5 2.0 1.7 1.8 AFFX- STAT1 signal transducer and activator of 25 16 4.3 1.3 0.9 1.6 1.0 HU- transcription 1, 91 kDa MISGF3A/ M97935_5_at 209823_x_at HLA-DQB1 major histocompatibility complex, 233 102 4.3 0.9 2.0 1.0 1.3 class II, DQ beta 1 204820_s_at BTN3A3 butyrophilin, subfamily 3, member A3 324 48 4.1 1.6 1.3 1.1 1.3 203868_s_at VCAM1 vascular cell adhesion molecule 1 285 164 4.1 0.8 1.9 0.9 1.4 211656_x_at HLA-DQB1 major histocompatibility complex, 421 132 4.1 1.0 1.6 1.3 1.2 class II, DQ beta 1 207485_x_at BTN3A1 butyrophilin, subfamily 3, member A1 55 28 4.0 2.0 1.7 2.1 1.1 202531_at IRF1 interferon regulatory factor 1 290 49 3.9 0.8 1.1 1.3 1.1 214234_s_at CYP3A5 cytochrome P450, family 3, subfamily 28 11 3.9 0.9 1.3 1.3 1.2 A, polypeptide 5 205114_s_at CCL3 chemokine (C-C motif) ligand 3 21 12 3.8 1.4 1.3 0.9 1.4 208451_s_at C4A complement component 4A 220 121 3.8 1.1 1.6 0.9 2.4 208747_s_at C1S complement component 1, s subcomponent 1786 602 3.6 1.3 1.0 1.0 1.7 205898_at CX3CR1 chemokine (C—X3—C motif) receptor 1 86 36 3.6 1.0 1.1 1.2 1.6 208071_s_at LAIR1 leukocyte-associated Ig-like receptor 1 37 25 3.5 0.5 3.6 1.1 0.8 208436_s_at IRF7 interferon regulatory factor 7 24 11 3.4 1.6 2.1 1.6 1.2 204858_s_at ECGF1 endothelial cell growth factor 1 (platelet- 78 30 3.3 1.9 1.1 1.7 1.4 derived) 209785_s_at PLA2G4C phospholipase A2, group IVC (cytosolic, 46 17 3.3 1.2 1.4 1.4 1.0 calcium-independent) 203052_at C2 complement component 2 205 16 3.3 0.9 1.1 1.1 1.2 204821_at BTN3A3 butyrophilin, subfamily 3, member A3 40 15 3.3 1.7 1.6 1.5 1.5 213095_x_at AIF1 allograft inflammatory factor 1 78 62 3.2 0.6 2.0 0.5 1.5 210164_at GZMB granzyme B (granzyme 2, cytotoxic T- 19 10 3.2 1.6 0.9 0.8 1.8 lymphocyte-associated serine esterase 1) 203882_at ISGF3G interferon-stimulated transcription factor 402 53 3.1 1.7 1.0 1.5 1.4 3, gamma 48 kDa 209901_x_at AIF1 allograft inflammatory factor 1 111 74 3.0 0.3 1.5 0.7 1.2 201891_s_at B2M beta-2-microglobulin 318 105 3.0 1.4 1.3 1.0 1.4 210072_at CCL19 chemokine (C-C motif) ligand 19 78 28 3.0 1.2 2.0 1.4 1.6 208893_s_at DUSP6 dual specificity phosphatase 6 107 40 3.0 1.0 1.2 0.9 1.1 217478_s_at HLA-DMA major histocompatibility complex, 838 145 2.9 1.1 1.4 1.2 1.3 class II, DM alpha 202705_at CCNB2 cyclin B2 43 14 2.9 1.4 1.2 1.4 1.3 215193_x_at HLA-DRB1 major histocompatibility complex, 1950 212 2.9 1.2 1.7 1.5 1.3 class II, DR beta 1 202687_s_at TNFSF10 tumor necrosis factor (ligand) super- 533 141 2.9 1.2 1.3 1.2 1.2 family, member 10 1405_i_at CCL5 chemokine (C-C motif) ligand 5 8 7 2.8 0.5 0.9 0.9 2.2 209619_at CD74 CD74 antigen (invariant polypeptide 922 192 2.8 1.1 1.3 1.0 1.3 of major histocompatibility complex, class II antigen-associated) 202688_at TNFSF10 tumor necrosis factor (ligand) super- 373 102 2.8 0.9 1.3 1.2 1.1 family, member 10 211367_s_at CASP1 caspase 1, apoptosis-related cysteine 53 13 2.7 1.4 1.2 1.2 1.3 protease (interleukin 1, beta, convertase) 204674_at LRMP lymphoid-restricted membrane protein 74 31 2.6 1.7 3.8 1.6 1.6 202436_s_at CYP1B1 cytochrome P450, family 1, subfamily 171 25 2.6 1.0 1.0 1.3 1.3 B, polypeptide 1 204006_s_at FCGR3A Fc fragment of IgG, low affinity IIIa, 41 19 2.5 0.8 1.3 1.0 1.5 receptor for (CD16) 214630_at CYP11B1 cytochrome P450, family 11, subfamily 25 12 2.5 0.8 1.1 1.0 0.9 B, polypeptide 1 210225_x_at LILRB3 leukocyte immunoglobulin-like receptor, 98 44 2.5 0.8 1.3 1.1 1.3 subfamily B (with TM and ITIM domains), member 3 206060_s_at PTPN22 protein tyrosine phosphatase, non- 23 11 2.5 1.0 2.5 1.2 0.9 receptor type 22 (lymphoid) 204116_at IL2RG interleukin 2 receptor, gamma (severe 188 27 2.4 1.4 3.8 1.3 1.2 combined immunodeficiency) 211528_x_at HLA-A major histocompatibility complex, 3314 497 2.4 1.3 1.1 1.3 1.1 class I, A 209813_x_at 39 22 2.4 0.4 1.2 1.0 1.0 214459_x_at HLA-C major histocompatibility complex, 4379 649 2.4 1.4 1.3 1.2 1.3 class I, C 216920_s_at TRGC2 T cell receptor gamma constant 2 72 13 2.4 1.0 1.6 1.1 1.2 211530_x_at HLA-A major histocompatibility complex, 868 214 2.3 1.8 1.5 1.6 1.6 class I, A 208894_at HLA-DRA major histocompatibility complex, 2704 518 2.3 1.0 1.3 1.2 1.1 class II, DR alpha 38241_at BTN3A3 butyrophilin, subfamily 3, member A3 36 8 2.3 14 1.1 1.0 1.2 205758_at CD8A CD8 antigen, alpha polypeptide (p32) 41 21 2.3 1.3 1.0 1.2 1.4 202644_s_at TNFAIP3 tumor necrosis factor, alpha-induced 183 50 2.3 1.2 1.7 1.3 1.7 protein 3 221875_x_at HLA-F major histocompatibility complex, 3883 622 2.3 1.2 1.0 1.2 1.0 class I, F 209970_x_at CASP1 caspase 1, apoptosis-related cysteine 168 26 2.3 1.1 1.2 1.1 1.5 protease (interleukin 1, beta, convertase) 203020_at HHL expressed in hematopoietic cells, heart, 575 183 2.2 1.1 1.4 1.0 0.8 liver 217362_x_at HLA-DRB6 major histocompatibility complex, 641 209 2.2 1.2 1.4 1.3 1.0 class II, DR beta 6 (pseudogene) 202465_at PCOLCE procollagen C-endopeptidase enhancer 1093 451 2.2 0.5 0.7 0.8 0.3 204057_at ICSBP1 interferon consensus sequence binding 67 21 2.2 1.4 1.6 1.2 1.1 protein 1 204890_s_at LCK lymphocyte-specific protein tyrosine 42 9 2.2 1.5 3.2 1.4 0.9 kinase 205926_at IL27RA interleukin 27 receptor, alpha 93 37 2.2 1.1 1.1 1.1 1.3 208200_at IL1A interleukin 1, alpha 14 10 2.2 1.1 0.4 1.6 1.2 206541_at KLKB1 kallikrein B, plasma (Fletcher factor) 1 59 32 2.2 1.2 1.1 1.0 1.2 208791_at CLU clusterin (complement lysis inhibitor, 3952 905 2.2 0.9 0.8 1.0 1.0 SP-40,40, sulfated glycoprotein 2, testosterone-repressed prostate message 2, apolipoprotein J) 201487_at CTSC cathepsin C 333 83 2.1 1.1 1.3 1.1 1.4 207857_at LILRB1 leukocyte immunoglobulin-like receptor, 30 15 2.1 0.5 1.0 0.7 0.8 subfamily B (with TM and ITIM domains), member 1 201422_at IFI30 interferon, gamma-inducible protein 176 14 2.1 1.0 1.3 1.1 1.4 30 204806_x_at HLA-F major histocompatibility complex, 3260 520 2.1 1.1 1.2 1.2 1.0 class I, F 210982_s_at HLA-DRA major histocompatibility complex, 743 100 2.1 1.0 1.5 1.1 1.0 class II, DR alpha 215485_s_at ICAM1 intercellular adhesion molecule 1 158 30 2.1 0.9 1.1 1.0 1.1 (CD54), human rhinovirus receptor 211529_x_at HLA-A major histocompatibility complex, 3620 317 2.1 1.2 1.2 1.3 1.2 class I, A 214377_s_at JAK1 Janus kinase 1 (a protein tyrosine 84 12 2.1 1.3 0.9 1.1 1.1 kinase) 202446_s_at PLSCR1 phospholipid scramblase 1 641 187 2.1 1.2 1.1 1.0 1.5 201743_at CD14 CD14 antigen 179 26 2.0 0.8 1.1 0.7 1.1 216526_x_at HLA-C major histocompatibility complex, 3770 1302 2.0 1.3 1.2 1.4 1.5 class I, C 202643_s_at TNFAIP3 tumor necrosis factor, alpha-induced 109 31 2.0 0.8 1.5 1.2 1.3 protein 3 206429_at F2RL1 coagulation factor II (thrombin) receptor- 26 15 2.0 2.0 1.1 1.8 1.5 like 1 211144_x_at TRGC2 T cell receptor gamma constant 2 64 16 2.0 1.3 1.2 1.3 1.0 209924_at CCL18 chemokine (C-C motif) ligand 18 23 20 2.0 0.7 1.5 2.3 1.6 212067_s_at C1R complement component 1, r 654 125 2.0 0.9 0.9 1.0 1.4 214511_x_at FCGR1A Fc fragment of IgG, high affinity Ia, 101 30 2.0 0.6 1.1 1.0 1.0 receptor for (CD64) 218009_s_at PRC1 protein regulator of cytokinesis 1 32 14 2.0 0.7 1.1 1.0 0.8 220040_x_at HCA127 hepatocellular carcinoma-associated 116 54 2.0 0.6 1.0 0.9 0.5 antigen 127 209365_s_at ECM1 extracellular matrix protein 1 156 44 2.0 1.3 0.9 1.0 1.1 210571_s_at CMAH cytidine monophosphate-N- 88 17 2.0 1.0 1.0 1.1 1.4 acetylneuraminic acid hydroxylase 213539_at CD3D CD3D antigen, delta polypeptide 85 39 1.9 2.0 3.2 1.7 1.9 (TiT3 complex) 209312_x_at HLA-DRB3 major histocompatibility complex, 3990 399 1.9 1.1 1.5 1.3 1.1 class II, DR beta 3 201315_x_at IFITM2 interferon induced transmembrane protein 1055 139 1.9 1.1 1.0 1.2 1.6 2 (1-8D) 209140_x_at HLA-B major histocompatibility complex, 8146 1478 1.9 1.1 1.1 1.2 1.2 class I, B 210865_at TNFSF6 tumor necrosis factor (ligand) super- 56 10 1.9 1.1 1.4 1.3 1.2 family, member 6 206360_s_at SOCS3 suppressor of cytokine signaling 3 95 30 1.8 0.8 0.9 1.0 1.4 211100_x_at LILRB1 leukocyte immunoglobulin-like receptor, 77 18 1.8 1.3 1.4 1.0 1.2 subfamily B (with TM and ITIM domains), member 1 203305_at F13A1 coagulation factor XIII, A1 polypeptide 183 27 1.8 0.9 0.9 1.1 1.3 209541_at IGF1 insulin-like growth factor 1 (somatomedin 524 255 1.8 0.9 0.9 0.9 1.2 C) 215313_x_at HLA-A major histocompatibility complex, 5166 264 1.8 1.3 1.2 1.3 1.3 class I, A 207238_s_at PTPRC protein tyrosine phosphatase, receptor 137 75 1.8 1.3 2.5 1.1 1.3 type, C 210864_x_at HFE hemochromatosis 159 24 1.8 1.2 1.1 1.3 0.9 219059_s_at XLKD1 extracellular link domain containing 1 286 63 1.8 1.0 1.5 1.0 1.4 211911_x_at HLA-B major histocompatibility complex, 5982 585 1.8 1.4 1.2 1.3 1.2 class I, B 206584_at LY96 lymphocyte antigen 96 75 32 1.8 1.1 1.4 1.0 1.4 202953_at C1QB complement component 1, q 227 37 1.8 1.0 1.1 1.0 1.1 subcomponent, beta polypeptide 211329_x_at HFE hemochromatosis 127 29 1.8 0.9 0.7 0.9 0.9 201858_s_at PRG1 proteoglycan 1, secretory granule 1003 235 1.8 0.8 1.0 0.9 1.1 208729_x_at HLA-B major histocompatibility complex, 5968 985 1.8 1.2 1.1 1.3 1.2 class I, B 211863_x_at HFE hemochromatosis 160 23 1.8 0.8 0.9 1.3 1.0 205859_at LY86 lymphocyte antigen 86 126 21 1.8 1.5 1.5 0.9 1.2 217456_x_at HLA-E major histocompatibility complex, 1205 148 1.8 1.2 1.2 1.4 1.2 class I, E 203028_s_at CYBA cytochrome b-245, alpha polypeptide 173 18 1.8 1.1 1.2 1.2 1.0 208018_s_at HCK hemopoietic cell kinase 98 36 1.8 1.2 1.4 1.1 1.2 208812_x_at HLA-C major histocompatibility complex, 4921 1097 1.8 1.3 1.2 1.1 1.2 class I, C 201508_at IGFBP4 insulin-like growth factor binding protein 4 2272 954 1.7 0.5 0.6 0.6 0.8 202803_s_at ITGB2 integrin, beta 2 (antigen CD18 (p95), 114 31 1.7 0.6 1.1 1.0 1.0 lymphocyte function-associated antigen 1; macrophage antigen 1 (mac-1) beta subunit) 204908_s_at BCL3 B-cell CLL/lymphoma 3 119 19 1.7 0.7 1.0 1.0 1.3 216217_at PLCL2 phospholipase C-like 2 28 8 1.7 1.0 1.0 0.9 1.0 205270_s_at LCP2 lymphocyte cytosolic protein 2 73 18 1.7 0.9 1.9 1.2 1.2 210754_s_at LYN v-yes-1 Yamaguchi sarcoma viral related 255 48 1.7 1.0 1.5 0.8 1.1 oncogene homolog 203332_s_at INPP5D inositol polyphosphate-5-phosphatase, 153 32 1.7 1.2 1.6 1.2 1.2 145 kDa 218232_at C1QA complement component 1, q 150 26 1.7 0.9 1.5 1.0 1.2 subcomponent, alpha polypeptide 208594_x_at LILRB3 leukocyte immunoglobulin-like receptor, 117 12 1.7 0.9 1.1 1.0 1.4 subfamily B, member 3 209348_s_at MAF v-maf musculoaponeurotic fibrosarcoma 280 67 1.7 1.0 1.3 0.9 0.9 oncogene homolog (avian) 201999_s_at TCTEL1 t-complex-associated-testis-expressed 785 133 1.7 0.9 1.0 0.9 0.9 1-like 1 204924_at TLR2 toll-like receptor 2 100 26 1.7 0.9 0.8 0.8 1.4 210176_at TLR1 toll-like receptor 1 68 18 1.7 0.9 1.3 0.8 1.3 202902_s_at CTSS cathepsin S 276 38 1.6 1.0 1.2 1.1 1.3 208829_at TAPBP TAP binding protein (tapasin) 318 51 1.6 0.9 1.1 1.1 1.0 202638_s_at ICAM1 intercellular adhesion molecule 1 256 83 1.6 0.8 0.8 0.9 1.5 (CD54), human rhinovirus receptor 212203_x_at IFITM3 interferon induced transmembrane protein 1101 194 1.6 1.4 1.1 1.0 1.3 3 (1-8U) 200905_x_at HLA-E major histocompatibility complex, 1308 239 1.6 1.3 1.1 1.2 1.2 class I, E 203923_s_at CYBB cytochrome b-245, beta polypeptide 183 20 1.6 0.9 1.2 1.0 1.2 204747_at IFIT4 interferon-induced protein with 123 21 1.6 0.9 0.8 0.9 0.9 tetratricopeptide repeats 4 209687_at CXCL12 chemokine (C—X—C motif) ligand 12 1071 254 1.6 1.0 1.1 0.8 1.3 (stromal cell-derived factor 1) 211332_x_at HFE hemochromatosis 134 13 1.6 1.0 0.8 1.1 0.9 211866_x_at HFE hemochromatosis 154 25 1.6 1.0 0.9 1.2 0.9 201859_at PRG1 proteoglycan 1, secretory granule 683 182 1.5 0.8 1.2 0.9 1.2 203932_at HLA-DMB major histocompatibility complex, 331 43 1.5 1.1 1.4 1.2 1.0 class II, DM beta 202450_s_at CTSK cathepsin K (pycnodysostosis) 415 68 1.5 1.3 1.0 1.2 1.3 203416_at CD53 CD53 antigen 296 119 1.5 1.2 2.3 1.0 1.4 213932_x_at HLA-A major histocompatibility complex, 1373 131 1.5 1.2 1.3 1.2 1.1 class I, A 208992_s_at STAT3 signal transducer and activator of 698 120 1.5 1.0 1.0 1.0 1.1 transcription 3 (acute-phase response factor) 219118_at FKBP11 FK506 binding protein 11, 19 kDa 184 39 1.5 0.6 1.0 0.8 0.8 210559_s_at CDC2 cell division cycle 2, G1 to S and G2 89 20 1.5 1.0 1.3 1.0 1.2 to M 218856_at TNFRSF21 tumor necrosis factor receptor super- 407 63 1.5 1.2 1.5 1.1 1.2 family, member 21 209049_s_at PRKCBP1 protein kinase C binding protein 1 314 40 1.5 1.0 1.2 0.9 1.0 213193_x_at TRB@ T cell receptor beta locus 214 67 1.5 1.2 2.4 1.2 1.2 204118_at CD48 CD48 antigen (B-cell membrane protein) 225 42 1.5 1.1 1.7 1.0 1.2 209753_s_at TMPO thymopoietin 110 41 1.5 0.8 0.9 0.9 1.1 200887_s_at STAT1 signal transducer and activator of 92 12 1.5 1.2 1.1 1.3 1.1 transcription 1, 91 kDa 203561_at FCGR2A Fc fragment of IgG, low affinity IIa, 128 46 1.5 1.1 1.4 0.8 1.4 receptor for (CD32) 209734_at HEM1 hematopoietic protein 1 196 24 1.5 1.2 1.5 1.0 1.1 AFFX- STAT1 signal transducer and activator of 51 11 1.4 1.1 1.1 1.3 1.0 HU- transcription 1, 91 kDa MISGF3A/ M97935_3_at 204852_s_at PTPN7 protein tyrosine phosphatase, non- 39 23 1.4 1.4 2.0 1.0 1.4 receptor type 7 211799_x_at HLA-C major histocompatibility complex, 1978 1219 1.4 1.0 1.0 1.5 0.8 class I, C 204232_at FCER1G Fc fragment of IgE, high affinity I, 513 73 1.4 0.9 1.3 1.0 1.5 receptor for; gamma polypeptide 218831_s_at FCGRT Fc fragment of IgG, receptor, transporter, 1066 183 1.4 1.0 1.0 1.0 0.9 alpha 216231_s_at B2M beta-2-microglobulin 9970 1299 1.4 1.2 1.3 1.1 1.2 219117_s_at FKBP11 FK506 binding protein 11, 19 kDa 772 157 1.4 0.7 0.8 0.7 0.7 217733_s_at TMSB10 thymosin, beta 10 8296 1670 1.4 1.1 1.1 1.1 1.2 203922_s_at CYBB cytochrome b-245, beta polypeptide 70 22 1.4 1.2 1.7 0.9 1.1 (chronic granulomatous disease) 203729_at EMP3 epithelial membrane protein 3 400 35 1.4 0.7 0.9 0.8 0.8 205298_s_at BTN2A2 butyrophilin, subfamily 2, member A2 259 22 1.4 1.1 1.2 1.3 1.1 220336_s_at GP6 glycoprotein VI (platelet) 39 14 1.4 1.2 1.1 1.2 1.0 200904_at HLA-E major histocompatibility complex, 700 284 1.4 1.1 1.2 1.0 1.2 class I, E 205831_at CD2 CD2 antigen (p50), sheep red blood 71 18 1.4 1.1 1.5 1.1 1.1 cell receptor 205098_at CCR1 chemokine (C-C motif) receptor 1 82 21 1.4 1.1 0.9 0.8 1.3 215990_s_at BCL6 B-cell CLL/lymphoma 6 (zinc finger 295 32 1.3 1.0 0.9 1.4 1.0 protein 51) 210514_x_at HLA-A major histocompatibility complex, 1046 84 1.3 1.2 1.1 1.2 1.0 class I, A 213869_x_at THY1 Thy-1 cell surface antigen 318 97 1.3 0.8 0.8 0.9 0.6 202637_s_at ICAM1 intercellular adhesion molecule 1 429 54 1.3 0.8 0.9 1.0 1.0 (CD54), human rhinovirus receptor 202957_at HCLS1 hematopoietic cell-specific Lyn substrate 1 174 17 1.3 1.2 1.5 1.0 1.1 209749_s_at ACE angiotensin I converting enzyme 1 76 25 1.3 0.9 0.8 0.8 1.1 210915_x_at TRB@ T cell receptor beta locus 176 34 1.3 1.3 2.5 1.3 1.0 209048_s_at PRKCBP1 protein kinase C binding protein 1 176 22 1.3 1.3 1.2 1.1 1.0 221978_at HLA-F major histocompatibility complex, 66 15 1.3 1.3 1.2 1.4 1.4 class I, F 210904_s_at IL13RA1 interleukin 13 receptor, alpha 1 354 81 1.2 1.1 0.9 1.1 0.9 203879_at PIK3CD phosphoinositide-3-kinase, catalytic, 160 25 1.2 1.1 1.7 1.1 1.3 delta polypeptide 204158_s_at TCIRG1 T-cell, immune regulator 1, ATPase, 163 37 1.2 1.0 0.9 1.0 1.0 H+ transporting, lysosomal V0 protein a isoform 3 52940_at SIGIRR single Ig IL-1R-related molecule 142 32 1.2 1.1 1.2 0.9 1.0

The strongest increase has been observed in veins (e.g., 20-fold for CXCL10 in pulmonary vein) and adrenal followed by arteries and heart tissues. Much less and irrelevant changes were observed in samples from liver, kidney, GIT, spleen, BM and cartilage. The fact that specific histopathological vascular findings have been observed only in veins and the genomic data show the presence of the specific pattern in all of the CV tissues tested, suggest that the genomic pattern (particularly, some soluble factors e.g., CXCL10 and CCL2) may be considered as early biomarkers for cox-2 inhibition-related CV side-effects or as early biomarkers for minimal (sub-clinical) vasculitis.

Vioxx® exhibits increased angiostatic and focal inflammatory effects predominantly in veins: The in vivo angiogenic effect of PGE2 is well documented experimentally and in particular by the fact that the EP4 receptor signalling has a major role in regulating closure or maintaining potency of the ductus arteriosus in newborns with congenital heart disease. Apart from this expected inhibition of angiogenic effects of PGE2 by coxibs tested in this analysis, Vioxx® strongly induced the expression of CXCL10, and PD-ECGF (both known anti-angiogenic proteins) mainly in iliac and pulmonary veins which suggests that a strong angiostatic effect occurred in the monkey #A60055.

The specific gene expression pattern observed in the monkey treated with Vioxx® strongly suggests the involvement of an endothelial cell tropic CMV-like infection or reactivation: (i) The expression of numbers of genes inducible by INFγ was strongly upregulated in most of the tissues from the Vioxx®-treated monkey. According to the literature, the induction of INFγ pathway is commonly observed during the first phase of CMV infection or reactivation. It has been shown that CMV antigen-stimulated CD4+ T cells from normal healthy CMV-seropositive donors secreted INFγ and TNF alpha, driving chemokines induction in endothelial cells. The strong INFγ pathway induction and histopathological findings of focal vasculitis in animal #A60055 together with the literature data indicate that latent endothelial cell tropic CMV infection might induces specific cellular immune responses, resulting in the induction of chemoatractants, leading to inflammation and endothelial cell injury. Bolovan-Fritts C A et al., J Virol. 78(23):13173-81 (December 2004).

(ii) In the vessels of the monkey A60055, expression of chemokines, mainly CXCL10, MCP-1 and at a lesser degree other chemokines e.g., CXCL9 and -11 were significantly upregulated (e.g., 150 fold increase for MCP-1 in pulmonary vein). It has been shown that atheroma-associated endothelial cells express CXCL10, CXCL9 and CXCL11. Their secretion from IFNγ-stimulated ECs is increased upon IL-1beta, TNF-alpha, and CD40 ligand treatments and decreased in the presence of nitric oxide. Mach F et al., J Clin Invest. 104(8):1041-50 (October 1999). These data suggest the involvement of these cytokines/chemokines in the pathogenesis/progression of inflammatory vascular changes such as arteriosclerosis or vasculitis. More interestingly, mouse CMV infection in an atherosclerosis animal model and in cholesterol-fed C57BL/6J mice significantly increases atherosclerotic lesion area and aortic expression of CXCL10, MCP-1, and other INF-gamma induced proteins. Burnett M S et al., Circulation. 109(7):893-7 (Feb. 24, 2004). Similarly, mouse CMV infection in the brains of immunodeficient mice, stimulates the production of CXCL10 and MCP-1. Cheeran M C et al., J Neurovirol. 10(3):152-62 (June 2004).

In light of these data, our results suggest that an endothelial cell tropic CMV-like reactivation might be the main factor involved in the initiation of the observed vascular changes in this analysis. Interestingly, human CMV encodes four chemokine receptors e.g., US28, which bind many of the human CC-chemokines, including RANTES, MCP-1, CCL3, and CXCL-11. As mentioned above, this class of chemokines contributes to the development of vascular disease such as atherosclerosis, restenosis, and transplant vascular sclerosis. The increased expression of these chemokines genes and/or their respective receptors (TABLE 1) in the monkey treated with Vioxx® raises the question whether they were produced by reactivated CVM virions or by INFγ activated endothelial cells as a result of inflammatory reaction to CMV infection.

Literature data also demonstrate that the induction of COX-2 and/or synthesis of PGE2 are essential for efficient CMV replication in human (Zhu H et al., Proc. Natl. Acad. Sci. USA 99:3932-3937 (2002)) and monkey (Rue C A et al., J Virol. 78(22):12529-36 (November 2004)). Interestingly, the rhesus cytomegalovirus (RhCMV) genome encodes a protein homologue to cellular cox-2 (vCOX-2). Experiments with vCOX-2 deleted RhCMV identified vCOX-2 as a critical determinant for endothelial cell tropism. Rue C A et al., J Virol. 78(22):12529-36 (November 2004).

The cPLA2, a key enzyme in arachidonic acid (AA) release, is the primary form of PLA2 responsible for the generation of PGE2, LTB4 and PAF from AA, in response to inflammatory stimuli. It has been established that cPLA2 exhibits antihypertrophic potential probably via signalling pathway of β2-ARs in heart. Pavoine C & Defer N, Cell Signal. 17(2):141-52 (February 2005). PLA2 signalling pathways has been shown to be involved in human CMV infection in several ways. (i) hCMV infection stimulates arachidonic acid metabolism associated with activation of PLA2 and a cellular cPLA2, (ii) both mRNAs encoding for cPLA2 and COX-2 are increased in infected cells, (iii) blocking the cellular pathway of PLA2 signalling inhibited hCMV infection, and recently (iv) it has been reported that a cPLA2 taken up by virus particles from infected cells plays a role in CMV infection at a post entry step. The inhibition of hCMV-borne cPLA2 had broader consequences on HCMV infection inhibiting the production of key viral antigens IE1, IE2 and pp65. In this monkey analysis, expression of cPLA2 was upregulated in most of the cardiovascular tissues from the Vioxx®-treated monkey only. Since all other monkeys showed no increase of cPLA2 expression, these data also suggest the presence/reactivation of a CMV infection in the endothelial cell of the Vioxx®-treated monkeys.

CMV is known as a strictly opportunistic pathogen, in immunocompetent individuals it is easily controlled yet never eliminated since a robust immune response suppresses persistent viral replication and facilitates a lifelong viral latency. In fact, CMV has several mechanisms to escape diverse host immune responses. CMV encodes for at least four proteins which interfere with classical MHC class I antigen presentation by preventing their cell surface expression, by transporting them to the cytosol, where they are degraded and by competing with TAP for the translocation of antigenic peptides to MHC molecules. However, evasion of MHC I is not perfect, since IFNγ activation by CMV can induce the synthesis of large quantities of MHC I and proteosomes that overwhelm viral inhibitory proteins and “rescue” the CTL response. Two CMV-encoded proteins also interact with non-classical MHC class I such as HLA-E, which leads to suppression of NK responses. CMV encode for the UL18 which has homology to MHC I heavy chain and is expressed on the cell surface. Disruption of UL18 severely restricts viral pathogenesis. CMV also interferes with MHC II presentation, which was strongly upregulated in the Vioxx®-treated monkey (TABLE 1). Classically, INF-gamma is a potent inducer of MHC II expression in many cell types including endothelial cells. However, some studies showed that in CMV-infected cells, IFN-gamma is unable to induce MHC II expression. Recently, MHC class II molecules expressed in EC have been proposed as the entry receptor for CMV. Thus, the protein expression of MHC class II molecules in tissue samples will be tested whether their increased mRNA expression are translated into functional proteins. CMV infection also induces alteration in the expression of important cytokines such as TNF, IGF beta and IL1 and upregulation of the complement control proteins CD46, and CD55. CMV also encodes for a surface Fc-receptor which can bind IgG with high affinity. Interestingly, expression of most of these genes including MHC molecules, several NK cell receptors, complement proteins, Fc receptors was significantly upregulated in the monkey #A60055. These results support the hypothesis that the specific expression pattern is probably induced by a CMV infection in the animal A60055 (TABLE 1).

The expression of Toll like receptor 2 and CD14 was significantly increased in several tissues from the Vioxx®-treated monkey. Recently, it has been shown that CMV activates inflammatory cytokine responses via TLR2/CD14 during the prereplication phase of the viral life cycle. Indeed, interferon and ISGs are robustly induced by CMV particles during entry via activation of IRF3, one of the key transcription factors for INFγ inducible genes. Later during the replication cycle, CMV encodes several chemokines and chemokine receptors that provide potent inflammatory signals. In fact, many of the pathological processes associated with CMV reactivation (including accelerated vascular disease, and graft rejection) appear to be mediated by the release of inflammatory cytokines. Compton T et al., J Virol. 77(8):4588-96 (April 2003). Even though other viruses (measles virus, and RSV), also activate innate responses in a TLR2/CD14-dependent manner, the overall expression pattern suggests that CMV infection/reactivation is probably responsible for the observed vasculitis in the veins of the Vioxx®-treated monkey.

CMV reactivation in the vascular system and use of anti-inflammatory compounds including NSAIDs and specific Cox-2 inhibitors: A number of infectious agents have been associated with atherosclerotic cardiovascular disorders, including CMV, Helicobacter pylori, EBV, HIV, HSV1, HSV2, and hepatitis B and C. Rue C A et al., J Virol. 78(22):12529-36 (November 2004). However, several reports in the literature suggest that the CMV infection/reactivation might be one of the major players in the pathogenesis of chronic inflammatory vascular diseases. For examples, rare cases of CMV vasculitis have been described even in healthy individuals, which may be associated with carotid intimal-medial thickening, or development of extensive mesenteric arterial and venous thrombosis. Other studies suggest that CMV infection or reactivation is involved in post-transplant sub endothelium/intramyocardial inflammation, atherogenesis, restenosis, and inflammatory abdominal aortic aneurysm. Koskinen P K et al., Transpl Infect Dis. 1(2):115-26 (June 1999)). Since ECs are one of the major targets for latent CMV infection, CMV induced lytic or inflammatory reaction in ECs may easily result in adherent thrombi formation in vivo. Thus, infection/reactivation of CMV in endothelial cells may cause vascular injury and promote the development of inflammation, atherosclerotic lesions, and thrombosis. Therefore, the observed vascular findings in this analysis might be the early indicators of a CMV vasculitis.

In line with our current observations on Vioxx® CV effect, Rott D et al., J Am Coll Cardiol. 41(10):1812-9 (May 21, 2003) found that inhibition of Cox-2 aggravated atherosclerosis in the apoE knockout mouse. The authors studied the effect of COX-2 inhibition on infectivity of cytomegalovirus and coincidentally showed increased disease burden in animals treated with the COX-2 inhibitor, including those not infected with the virus. According to the FitzGerald hypothesis (see BACKGROUND OF THE INVENTION), this should reflect selective suppression of PGI2 and an unopposed effect of TXA2, however, the authors suggest an alternative hypothesis indicating that the suppression of anti-inflammatory PGs, such as PGJ2, and its metabolite 15-deoxy-delta12,14-PGJ2 might also result in this type of vascular changes. Rott D et al., J Am Coll Cardiol. 41(10):1812-9 (May 21, 2003). Another hypothesis might be that Cox-2 specific inhibitors but also NSAIDs can also initiate or aggravate atherosclerotic changes by inhibiting the production of PGE2 leading to the reactivation of latent CMV infection. In fact, it has been clearly documented that PGE2 can inhibit replication of viruses including CMV and HIV-1 through activation of cAMP and PKA which are the key enzymes in the negative regulation of immune responses and a potential target for inhibiting autoreactive T cells. Aandahl E M et al., J. Immunol. 169(2):802-8 (Jul. 15, 2002). Other reports support this hypothesis showing that PGE-2 suppresses chemokine production by increasing cAMP trough the EP4 receptor. Takayama K et al., J Biol Chem.277(46):44147-54 (Nov. 15, 2002). It has been shown that PGE2 activated cAMP/PKA inhibits INFγ signalling pathway proteins (JAK-1 and STAT1) and consequently decrease chemokine synthesis such as CXCL10. Kanda N et al., J Invest Dermatol. 119(5):1080-9 (November 2002).

More interestingly, a selective cox-2 inhibitor, NS398, potentiates CXCL10 synthesis upon INFγ stimulation by preventing PGE2 production and PKA activation. Wright K L et al., Br J Pharmacol. 141(7):1091-7 (April 2004). In our analysis, the significant activation of numbers of INFγ inducible genes even in vascular tissues where there was no histopathological abnormalities suggest that Vioxx® has similar potentialization effect on the INFγ pathway activation as described for NS398. Thus, the Vioxx® treatment might lower the threshold for the generation of a chronic vascular inflammation via inhibition of PGE2 and activation of INFγ pathways triggered by reactivation of a latent CMV infection in endothelial cells. It is noteworthy that the CMV seropositivity has been reported in most of the monkey strains and in about 60-70% of healthy individuals. Overall, the data suggest that inhibition of Cox-2 and in particular PGE2 by Vioxx® might results in an uncontrollable/continuous production of soluble factors induced by INFγ pathway activation. The INFγ pathway is commonly induced in case of endothelial/vascular tropic virus infection including some isolates of CMV. As suggested by the presently observed findings, activation of vascular endothelium and attraction of specific blood cells by chemokines (e.g., CXCL10, MCP-1, often activated during a CMV infection) might increase their interaction leading to cardiovascular adverse effects.

The histopathological examination revealed marginal vascular changes consistent with the genomic findings and suggesting that the specific genomic pattern is an early signature of vasculitis and is observed only in the monkey treated with Vioxx® (FIG. 3).

Soluble proteins present in serum and plasma of the same monkeys have been measured using a multiplex assay produced by Rules-Based Medicine (RBM®) of Texas. The results were in line with the genomic results showing the increased level of INFγ inducible proteins only in the Vioxx®-treated monkey (FIG. 5).

Increased expression of CXCL10 chemokine and INFγ has been confirmed by an ELISA both in serum and plasma from the Vioxx®-treated monkey (FIG. 6 and FIG. 7). These peripheral biomarkers might allow safe use of cox-2 inhibitory compounds in clinics and selection of cox-2 inhibitory follow-up compounds without cardiovascular toxicity.

Localisation of several proteins (e.g., PD-ECGF1) at the site of vascular lesion indicates the specificity of changes for a vasculopathy (FIG. 8). The genomic and serum/plasma protein signature identified in this analysis predicts for a minimal and focal vasculitis and may be used for patient's monitoring of vasculitis induced by different compounds/drugs (e.g., phosphodiesterase inhibitors) or occurring during vascular or autoimmune disorders.

CONCLUSION

Overall genomic data showed that the Vioxx®-treated animals, and in particular the animal #60055 exhibit a specific mRNA expression pattern which strongly suggest the induction of an intravascular procoagulative/prothrombotic state particularly in venous vessels of the Vioxx®-treated animals. The specific genomics pattern includes genes involved in blood and endothelial cell activation, interaction between blood and ECs, strong activation of INFγ pathway, and release of pro-inflammatory cytokines and chemo-attractants. These data together with biochemical and histopathological findings suggest that Vioxx® may exaggerate host immune response during some/specific viral infection(s) with endothelial tropism, suggestively reactivation of a CMV infection.

Our hypothesis is that the inhibition of Cox-2/PGE2 results in decreased level of cAMP and PKA and consequently in an uncontrollable/continuous production of soluble factors via INFγ pathways induced by a CMV infection in endothelial/blood cells. Activation of vascular endothelium and attraction of specific blood cells by chemokines should further increase their interaction leading to prothrombotic events and increasing the risk of cardiovascular adverse events. Indeed, the majority of these changes have been shown to be directly involved in the pathogenesis of diverse cardiovascular diseases including atherosclerosis, CAD, and thrombosis. Preliminary histopathological results confirmed the genomic finding showing that the specific genomics pattern is an early signature of vasculitis and observed only in the animal(s) treated with Vioxx®.

Identification of biomarkers might allow the safe use of cox-2 inhibitory compounds in clinics and selection of cox-2 inhibitory follow-up compounds without cardiovascular toxicity. Indeed, several of the gene increases in the vessels of the Vioxx®-treated animal encode for secreted proteins, e.g., CXCL10, other chemokines, which can be measured in peripheral samples such as blood or urine. If a CMV reactivation (or other endothelium tropic virus infection) is confirmed, a vaccination strategy prior to administration of Cox-2 inhibitory therapies might be an alternative approach for improving the CV therapeutic and safety profile of this class of compounds.

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All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes. In addition, all Affymetrix identification numbers for each probe set corresponding to each gene changes cited herein (TABLE 1) are incorporated herein by reference in their entirety and for all purposes to the same extent as if each such number was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.

EQUIVALENTS

The present invention is not to be limited in terms of the particular embodiments described in this application, which are intended as single illustrations of individual aspects of the invention. Many modifications and variations of this invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatus within the scope of the invention, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing description and accompanying drawings. Such modifications and variations are intended to fall within the scope of the appended claims. The present invention is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. 

1. A method for detecting the presence of minimal or early vasculitis or other vasculopathies in a subject, comprising the steps of: (a) obtaining a sample from a subject to whom a compound or drug, susceptible to induce cardiovascular pathologies has been administered or a subject with a vascular autoimmune disorder; (b) analysing the sample for the presence of a biomarker of minimal or early vasculitis or other vasculopathies; and (c) determining whether the subject has minimal or early vasculitis or other vasculopathies based upon the presence of absence of a biomarker of minimal or early vasculitis or other vasculopathies.
 2. A method for predicting compound or drug-induced cardiovascular adverse effects in a subject to whom a cox-2 inhibitory compound or drug has been administered, comprising the steps of: (a) obtaining a sample from a subject to whom a cox-2 inhibitory compound or drug has been administered; (b) analysing the sample for the presence of a biomarker of cardiovascular adverse effects; and (c) determining whether the subject has cox-2 inhibitor-induced cardiovascular adverse effects based upon the presence of absence of a biomarker of cardiovascular adverse effects.
 3. The use of a cox-2 inhibitory compound in the manufacture of an anti-inflammatory medicament with a reduced risk of cardiovascular toxicity, wherein the use comprises the steps of: monitoring the patient to whom the anti-inflammatory medicament has been administered for the presence or absence of biomarkers predictive of cox-2 inhibitor-induced cardiovascular adverse effects.
 4. The method of claim 3, wherein cox-2 inhibitory compound is selected from the group consisting of cox-2 specific inhibitors (coxibs), classical NSAE)S, other antiinflammatory/immunosuppressive/immunomodulatory compounds and direct PGE2, cAMP and PKA inhibitors.
 5. The method of claim 3, wherein cox-2 inhibitory compound is selected from the group consisting of COX1 89 (Lumiracoxib®), refocoxib (Vioxx®), and celecoxib (Celebrex®).
 6. The method of claim 3, wherein the cox-2 inhibitory compound is the non specific cox-2 inhibitory compound diclofenac (Voltaren®).
 7. The method of claim 3, wherein the biomarker predictive of cox-2 inhibitor-induced cardiovascular adverse effects is an increase in gene expression of a gene selected from the genes listed in TABLE
 1. 8. The method of claim 3, wherein the biomarker predictive of cox-2 inhibitor-induced cardiovascular adverse effects is an increase in gene expression of an interferon inducible gene selected from the group consisting of the genes encoding for Toll-like receptors (TLRs), classical and non-classical MHC class I proteins, MHC class II proteins, TcRs, NK receptors, CXCL10, CXCL-9, CXCL 11, MCP-I (CCL2), Jak1 and Stat1.
 9. The method of claim 3, wherein the biomarker predictive of cox-2 inhibitor-induced cardiovascular adverse effects is an increase in gene expression of the gene for a coagulation pathways-related molecule selected from the group consisting of PD-ECGF, coagulation factor II (thrombin) receptor-like 1 and Factor 13 A1.
 10. The method of claim 3, wherein the biomarker predictive of cox-2 inhibitor-induced cardiovascular adverse effects is an increase in Cc110 gene expression.
 11. The method of claim 3, wherein the biomarker predictive of cox-2 inhibitor-induced cardiovascular adverse effects is an increase in the release of pro-inflammatory cytokines and chemo-attractants.
 12. The method of claim 3, wherein the biomarker predictive of cox-2 inhibitor-induced cardiovascular adverse effects is an increase in INFγ inducible proteins.
 13. The method of claim 3, wherein the biomarker predictive of cox-2 inhibitor-induced cardiovascular adverse effects is an increase in CXCL10 (IP1O) protein levels.
 14. The method of claim 3, wherein the biomarker predictive of cox-2 inhibitor-induced cardiovascular adverse effects is an increase in PD-ECGF1 protein.
 15. The method of claim 3, wherein the biomarker predictive of cox-2 inhibitor-induced cardiovascular adverse effects is an increase in cPLA2 protein.
 16. The method of claim 3, wherein the sample is a tissue sample.
 17. The method of claim 3, wherein the sample is a cardiovascular tissue sample.
 18. The method of claim 3, wherein the sample is selected from the group consisting of blood, plasma, serum, urine and saliva.
 19. A method for the selection of cox-2 inhibitory compounds without cardiovascular toxicity for use in patients, comprising the steps of: (a) administering a cox-2 inhibitory compound to a subject; (b) monitoring of early changes predictive of cardiovascular adverse effects in patients treated with compounds exhibiting cox-2 inhibition or increasing the production of molecules induced by interferons or by virus infections or vascular autoimmune disorders resulting in pro-coagulative/prothrombotic/endothelium changes; (c) selecting the cox-2 inhibitory compounds that do not show cardiovascular toxicity for use in patients; and (d) selection of sub-population of patients to be treated safely by cox-2 inhibitory compounds/drugs
 20. The method of claim 19, wherein the subject is a cynomolgous monkey.
 21. A vaccination strategy prior to administration of cox-2 inhibitor to a subject, wherein the vaccination strategy reduces cardiovascular toxicity in the subject to whom the cox-2 inhibitor is administered. 